pass: Detection of multivariate anomalous segments using PASS.

In anomaly: Detecting Anomalies in Data

Detection of multivariate anomalous segments using PASS.

Description

Implements the PASS (Proportion Adaptive Segment Selection) procedure of Jeng et al. (2012). PASS uses a higher criticism statistic to pool the information about the presence or absence of a collective anomaly across the components. It uses Circular Binary Segmentation to detect multiple collective anomalies.

Usage

pass(x, alpha = 2, lambda = NULL, max_seg_len = 10, min_seg_len = 1)

Arguments

x	A numeric matrix with n rows and p columns containing the data which is to be inspected. The time series data classes ts, xts, and zoo are also supported.
alpha	A positive integer > 0. This value is used to stabilise the higher criticism based test statistic used by PASS leading to a better finite sample familywise error rate. Anomalies affecting fewer than alpha components will however in all likelihood escape detection. The default is 2.
lambda	A positive real value setting the threshold value for the familywise Type 1 error. The default value is (1.1 {\rm log}(n \times max\_seg\_len) +2 {\rm log}({\rm log}(p))) / \sqrt{{\rm log}({\rm log}(p))}.
max_seg_len	A positive integer (max_seg_len > 0) corresponding to the maximum interval length. This parameter corresponds to L in Jeng et al. (2012). The default value is 10.
min_seg_len	A positive integer (max_seg_len >= min_seg_len > 0) corresponding to the minimum segment length. The default value is 1.

Value

An instance of an S4 object of type .pass.class containing the data X, procedure parameter values, and the results.

References

\insertRef

10.1093/biomet/ass059anomaly

\insertRef

JSS-anomaly-paper-finalanomaly

Examples

library(anomaly)
# generate some multivariate data
data(simulated)
res<-pass(sim.data)
summary(res)
plot(res,variate_names=TRUE)

anomaly documentation built on Sept. 11, 2024, 9:08 p.m.